A reliability evaluation method and system of an intelligent traffic management system

Abstract

The application discloses a kind of reliability evaluation method and system of intelligent traffic management system, it is related to traffic management technical field, in the system operation, the function of each intelligent traffic management system is analyzed, the failure and abnormal condition of intelligent traffic management system are identified by analyzing each module and determine the cause, relevant data in system operation is collected by collecting intelligent traffic management system, and failure mode evaluation is carried out, data set is calculated and analyzed, to be modeled training by characteristic data, by model training acquisition: risk index Fxzs, failure index Gzzs, safety index Aqzs, by the influence degree and probability of failure, the risk level corresponding to each failure mode is determined, for high-risk failure, corresponding improvement measures are formulated, the effectiveness and reliability of improvement measures are ensured by testing and verifying the improved system, the expected effect achieved by verification improvement measures, to ensure the stability of system.

Description

Technical Field

[0001] This invention relates to the field of traffic management technology, specifically to a reliability evaluation method and system for an intelligent traffic management system. Background Technology

[0002] With the rapid pace of urbanization and the continuous increase in transportation vehicles in my country, urban traffic is becoming increasingly congested, and traffic accidents are occurring more frequently. Traditional traffic management methods are no longer adequate to meet the growing traffic demands. This system integrates functions such as traffic monitoring, data analysis, signal control, and navigation planning. It can collect and analyze traffic data in real time, automatically adjust the timing sequence of traffic lights, and provide real-time navigation and route planning services, thereby achieving more efficient, safe, and environmentally friendly urban traffic management. Furthermore, with the advancement of intelligent technologies, we will see the large-scale application of more intelligent traffic management systems in future urban traffic management.

[0003] Traditional traffic management typically employs fixed-pattern scheduling strategies. However, this approach often proves inadequate when faced with complex and ever-changing real-world traffic conditions. When this system malfunctions, it not only exacerbates traffic congestion but also disrupts traffic order around schools, further increasing travel time for students and parents and raising the risk of traffic accidents. If the same systemic problems occur in high-traffic commercial areas, they can further worsen traffic congestion and impact pedestrian flow, ultimately reducing the overall operational efficiency of the commercial district. Summary of the Invention

[0004] (a) Technical problems to be solved

[0005] To address the shortcomings of existing technologies, this invention provides a reliability evaluation method and system for intelligent traffic management systems, solving the problems mentioned in the background section.

[0006] (II) Technical Solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solution: a reliability evaluation method and system for an intelligent traffic management system, comprising a functional analysis module, a fault mode identification module, an information collection module, a fault impact assessment module, a risk level determination module, an improvement measure formulation module, and a testing and verification module;

[0008] The functional analysis module is used to perform detailed analysis of each functional module of the intelligent traffic management system to understand the overall structure and function of the system, including traffic monitoring, data analysis, signal control and navigation planning. The functional analysis module provides basic information for subsequent evaluation.

[0009] The fault mode identification module is used to identify fault modes and abnormal situations that occur in the intelligent traffic management system. By analyzing each functional module, the causes and situations leading to system faults are determined.

[0010] The information collection module is used to collect relevant data from the intelligent traffic management system during system operation, including fault information and performance data, and package them into a dataset to send to the fault impact assessment module.

[0011] The fault impact assessment module evaluates the fault modes of the information collection module, calculates and analyzes the dataset, and then uses the feature data to train a model to obtain the following: risk index Fxzs, fault index Gzzs, and safety index Aqzs.

[0012]

[0013] In the formula, Fxzs represents the risk index, Gzzs represents the failure index, Aqzs represents the safety index, Nx represents the service life of the equipment, α represents the temperature value, and β represents the humidity value;

[0014] The real-time ambient temperature Hjwd is compared with the preset operating temperature Yswd to obtain the temperature factor wdyz. The weather type tqlx and the equipment impact result yxjg are matched to obtain the weather factor tqyz. The temperature factor wdyz and the weather factor tqyz are correlated and fitted into the environmental coefficient hjxs. The road type dllx and the surrounding building type jzlx are matched to obtain the road factor dlyz. The population density value rkmd around the road and the residential area size jzgm are matched to obtain the population factor rkyz. The road factor dlyz and the population factor rkyz are correlated and fitted into the traffic flow coefficient Jtll.

[0015] The failure rate gzl and the mean time between failures wgzsc are matched to obtain the failure factor gzyz. The failure rate sxl and the mean repair time xfsc are matched to obtain the repair factor xfyz. The failure factor gzyz and the repair factor xfyz are correlated to form the availability coefficient kyxs. The availability coefficient kyxs and the failure mode gzms are matched and correlated to form the safety index Aqzs.

[0016] The risk level determination module determines the corresponding risk level for each fault mode based on the degree and probability of the fault, thereby classifying and sorting the faults, and high-risk faults need to be dealt with first.

[0017] The improvement measure formulation module is used to formulate corresponding improvement measures for high-risk faults. These measures include technical optimization, backup mechanisms and fault recovery strategies, which aim to reduce the probability of faults or mitigate the impact of faults.

[0018] The testing and verification module is used to test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. By testing the system, the expected effects of the improvement measures are verified, thereby ensuring the stability and reliability of the system.

[0019] Preferably, the functional analysis module includes a module decomposition unit, a functional description unit, and a module classification unit;

[0020] The module decomposition unit is used to decompose the intelligent traffic management system into various functional modules, dividing the complex intelligent traffic management system into relatively independent functional modules, including traffic monitoring, data analysis, signal control and navigation planning;

[0021] The functional description module is used to describe each functional module in detail, clarifying their role, function and task;

[0022] The module classification unit is used to classify and summarize functional modules based on their similarity and relevance, so as to better understand the structure and composition of the system.

[0023] Preferably, the fault mode identification module includes a historical data analysis unit, a system testing unit, and a fault list generation unit;

[0024] The historical data analysis unit is used to analyze historical fault data, identify past fault modes and anomalies in the system, and identify common fault modes in the system through statistical analysis of historical fault data, so as to facilitate subsequent risk assessment and optimization measures.

[0025] The system testing unit is used to perform various tests on the system, simulate different operating scenarios, discover potential failure modes and abnormal behaviors, and verify the stability and performance of the system under different conditions through system testing and experiments, so as to identify possible failure situations.

[0026] The fault list generation unit is used to generate a fault list of the system based on historical data analysis and system test results. It integrates historical fault data and test results to form a fault list of the system, which can be used for subsequent risk assessment and improvement measures.

[0027] Preferably, the information acquisition module includes a sensor data acquisition unit and an external data interface unit;

[0028] The sensor data acquisition unit is used to collect data from various sensors in the traffic management system. By connecting to various sensor devices, it can obtain the data required by the traffic management system in real time, including traffic flow, vehicle location and environmental information. It matches the weather type tqlx and the equipment impact result yxjg to obtain the weather factor tqyz.

[0029] Among them, the sequence information of the impact information of the weather on the equipment operating status is matched according to the weather type tqk, and the recorded sequence information is marked as the equipment impact result yxjg;

[0030] The external data interface unit is used to obtain data from external data sources, including weather data and traffic event data. By connecting to external data sources, the external data interface unit obtains real-time relevant data from external data interfaces to supplement the internal data of the system and help to more comprehensively analyze and evaluate traffic conditions.

[0031] Among them, the temperature factor Wdyz includes: real-time ambient temperature Hjwd and preset operating temperature Yswd; the weather factor Tqyz includes weather type tqlx and equipment impact result yxjg; the road factor dlyz includes road type dllx and surrounding building type jzlx; and the population factor rkyz includes the population density value around the road rkmd and the residential area size jzgm.

[0032] Preferably, the fault impact assessment module includes a fault consequence analysis unit, a risk assessment unit, and an optimization scheme formulation unit;

[0033] The failure consequence analysis unit is used to build a data model based on the acquired dataset for analysis and prediction, in order to obtain the risk index Fxzs, failure index Gzzs, and safety index Aqzs.

[0034] The fault index Gzzs is obtained using the following formula:

[0035]

[0036] Gzzs = Hjxs + Jtll + A;

[0037] In the formula, Hjxs represents the environmental coefficient, Jtll represents the traffic flow coefficient, Wdyz represents the temperature factor, Tqyz represents the weather factor, Hjwd represents the real-time environmental temperature, Yswd represents the preset operating temperature, tqlx represents the weather type, yxjg represents the equipment impact result, dlyz represents the road factor, dllx represents the road type, jzlx represents the surrounding building type, rkyz represents the population factor, rkmd represents the population density value around the road, jzgm represents the residential area size, Gzzs represents the failure index, Aqzs represents the safety index, Nx represents the service life of the equipment, α represents the temperature value, βa1, a2, a3 and a4 represent the weight values ​​of the environmental coefficient Hjys, the traffic flow coefficient Jtll and the service life of the equipment Nx respectively, and A represents the correction constant;

[0038] Wherein, 0.55≤Hjxs≤0.75, 0.55≤Jtll≤0.75, 0.35≤Nx≤0.55, 0.35≤Wdyz≤0.55, 0.35≤Tqyz≤0.55, 0.35≤Dlyz≤0.55, 0.35≤rkmd≤0.55, and a1+a2+a3+a4≥2, and A represents the correction constant;

[0039] The risk assessment unit is used to comprehensively assess various faults by combining the probability of fault occurrence and the consequences of faults, determine their risk level to the system, and formulate corresponding countermeasures.

[0040] The optimization scheme formulation unit is used to prioritize high-risk faults based on the risk assessment results, and formulate corresponding technical optimization measures, backup mechanism measures and fault recovery strategies to maximize the reliability and stability of the system.

[0041] Preferably, the security index Aqzs is obtained using the following formula:

[0042]

[0043] In the formula, kyxs represents the availability coefficient, gzyz represents the failure factor, xfyz represents the repair factor, gzms represents the failure mode, w1 and w2 represent the weight values ​​of the availability coefficient kyxs and the failure mode gzms, respectively, and M represents the correction constant.

[0044] Wherein, 0.65≤Kyxs≤0.85, 0.45≤Gzms≤0.65, w1+w2≥1.0, and M represents the correction constant;

[0045] Among them, the available coefficients Kyxs include: fault factor gzyz and repair factor xfyz.

[0046] Preferably, the risk level determination module includes a risk level assessment unit;

[0047] The risk level assessment unit compares the risk index Fxzs with a preset risk threshold to determine the risk level of the system.

[0048] By comparing the risk index Fxzs with the preset threshold standard, a graded strategy scheme is obtained:

[0049] If Fxzs≤19, optimization level 1 is obtained, the risk is extremely low, and the current state can be maintained;

[0050] 20≤Fxzs≤39, achieving optimization level two, with low risk, and performing preventative maintenance for potential low-risk faults;

[0051] 40≤Fxzs≤59, achieving optimization level three, medium risk, strengthening fault monitoring and diagnosis, and developing backup and emergency recovery plans;

[0052] 60≤Fxzs≤79, achieving optimization level four, which carries a relatively high risk; therefore, it is necessary to strengthen preventive measures against high-risk faults.

[0053] If 80≤Fxzs≤99, optimization level 5 is obtained, which is extremely risky, and emergency measures should be taken.

[0054] Preferably, the improvement measure formulation module includes an optimization scheme generation unit and a scheme evaluation and implementation unit;

[0055] The optimization scheme generation unit is used to formulate targeted optimization schemes and improvement measures based on the risk level and the specific circumstances of the system.

[0056] The scheme evaluation and implementation unit is used to evaluate the formulated optimization scheme and implement it into the intelligent traffic management system.

[0057] Preferably, the testing and verification module includes a functional testing unit and a performance verification unit;

[0058] The functional testing unit is used to perform functional tests on each functional module of the intelligent traffic management system to verify whether the system works normally according to the design requirements.

[0059] The performance verification unit verifies and evaluates the performance of the intelligent traffic management system to confirm whether the system's performance indicators meet the requirements.

[0060] A reliability evaluation method for an intelligent traffic management system includes the following steps:

[0061] Step 1: Through detailed analysis of each functional module of the intelligent traffic management system, understand the overall structure and function of the system;

[0062] Step 2: By identifying the fault modes and abnormal situations that occur in the intelligent traffic management system, determine the causes and circumstances leading to the system failures;

[0063] Step 3: Collect relevant data during the operation of the intelligent traffic management system;

[0064] Step 4: By performing fault mode assessment on the information collection module and calculating and analyzing the dataset, model training is conducted using feature data to obtain the following: risk index Fxzs, fault index Gzzs, and safety index Aqzs.

[0065] Step 5: Determine the corresponding risk level for each failure mode based on the degree and probability of the failure, thereby classifying and ranking the failures;

[0066] Step Six: Develop corresponding improvement measures for high-risk faults to reduce the probability of faults or mitigate their impact;

[0067] Step 7: Test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. By testing the system, verify the expected effects of the improvement measures, thereby ensuring the stability and reliability of the system.

[0068] (III) Beneficial Effects

[0069] This invention provides a reliability evaluation method and system for an intelligent traffic management system. It has the following beneficial effects:

[0070] (1) During system operation, analyze the various functions of the intelligent traffic management system, identify the faults and abnormalities that occur in the intelligent traffic management system by analyzing each module and determine the causes, collect relevant data during the operation of the intelligent traffic management system to conduct fault mode assessment, calculate and analyze the dataset, and then use feature data to model and train, and obtain the following through model training: risk index Fxzs, fault index Gzzs, and safety index Aqzs. Based on the degree and probability of the fault, determine the corresponding risk level for each fault mode, thereby classifying and sorting the faults. High-risk faults need to be dealt with first. For high-risk faults, formulate corresponding improvement measures to reduce the probability of faults or mitigate the impact of faults. Test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. Through the reliability evaluation of the system, predict the stability and reliability of the system, maintain the traffic order of roads around the campus, improve traffic flow, reduce traffic congestion and accident rate as much as possible, and reduce traffic delay time, providing a safer and more convenient travel environment for students and parents.

[0071] (2) Through real-time data collection and intelligent decision-making, the intelligent traffic management system can optimize traffic signal timing and scheduling, thereby improving traffic flow, reducing traffic disorder around schools, extending the travel time of students and parents, increasing the risk of traffic accidents, promoting the improvement of urban traffic operation efficiency, shortening commuting time, increasing road capacity, and reducing the risk of traffic accidents. This will allow residents and travelers to enjoy a smoother traffic experience in their daily travels. Through fault identification and early warning, the intelligent traffic management system can monitor traffic flow and behavior in real time, help to discover potential risks of traffic accidents in a timely manner, and take corresponding measures to avoid accidents. This will improve traffic safety, reduce traffic accidents and casualties, and protect the lives and property of residents and travelers.

[0072] (3) By eliminating or reducing the probability of failure, the risk of the system stopping due to failure is reduced, the system stability and availability are improved, and traffic flow is optimized, traffic congestion is reduced, and road capacity is improved through more accurate data analysis and intelligent signal control. Attached Figure Description

[0073] Figure 1 This is a schematic diagram of the reliability evaluation method and system block diagram of an intelligent traffic management system according to the present invention.

[0074] Figure 2 This is a schematic diagram of the reliability evaluation method and system steps of an intelligent traffic management system according to the present invention; Detailed Implementation

[0075] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0076] With the acceleration of urbanization and the continuous increase in transportation vehicles, urban traffic congestion and frequent traffic accidents are becoming increasingly prominent problems. Traditional traffic management methods are no longer able to meet the growing traffic demands. This system integrates multiple functional modules such as traffic monitoring, data analysis, signal control, and navigation planning. By collecting and analyzing traffic data in real time, it automatically adjusts the timing of traffic lights and provides real-time navigation and route planning functions to achieve more efficient, safe, and environmentally friendly traffic management. With the development of intelligent technology, intelligent traffic management systems have broad application prospects in the field of urban transportation.

[0077] Traditional traffic management systems typically employ static scheduling strategies, which struggle to cope with complex and ever-changing traffic conditions. Deploying intelligent traffic management systems around schools can enable traffic flow monitoring and optimization, ensuring the safety and convenience of students and parents. However, system malfunctions can lead to increased traffic congestion, chaotic traffic around schools, longer travel times for students and parents, and increased risk of traffic accidents. Furthermore, system failures in commercial centers can cause traffic congestion, impacting customer traffic in commercial areas and reducing the operational efficiency of businesses.

[0078] Example 1

[0079] This invention provides a reliability evaluation method and system for an intelligent traffic management system. Please refer to [link / reference]. Figure 1 It includes a functional analysis module, a fault mode identification module, an information collection module, a fault impact assessment module, a risk level determination module, an improvement measure formulation module, and a testing and verification module;

[0080] The functional analysis module is used to perform detailed analysis of each functional module of the intelligent traffic management system to understand the overall structure and function of the system, including traffic monitoring, data analysis, signal control and navigation planning. The functional analysis module provides basic information for subsequent evaluation.

[0081] The fault mode identification module is used to identify fault modes and abnormal situations that occur in the intelligent traffic management system. By analyzing each functional module, the causes and situations leading to system faults are determined.

[0082] The information collection module is used to collect relevant data from the intelligent traffic management system during system operation, including fault information and performance data, and package them into a dataset to send to the fault impact assessment module.

[0083] The fault impact assessment module evaluates the fault modes of the information collection module, calculates and analyzes the dataset, and then uses the feature data to train a model to obtain the following: risk index Fxzs, fault index Gzzs, and safety index Aqzs.

[0084]

[0085] In the formula, Fxzs represents the risk index, Gzzs represents the failure index, Aqzs represents the safety index, Nx represents the service life of the equipment, α represents the temperature value, and β represents the humidity value;

[0086] The real-time ambient temperature Hjwd is compared with the preset operating temperature Yswd to obtain the temperature factor wdyz. The weather type tqlx and the equipment impact result yxjg are matched to obtain the weather factor tqyz. The temperature factor wdyz and the weather factor tqyz are correlated and fitted into the environmental coefficient hjxs. The road type dllx and the surrounding building type jzlx are matched to obtain the road factor dlyz. The population density value rkmd around the road and the residential area size jzgm are matched to obtain the population factor rkyz. The road factor dlyz and the population factor rkyz are correlated and fitted into the traffic flow coefficient Jtll.

[0087] The failure rate gzl and the mean time between failures wgzsc are matched to obtain the failure factor gzyz. The failure rate sxl and the mean repair time xfsc are matched to obtain the repair factor xfyz. The failure factor gzyz and the repair factor xfyz are correlated to form the availability coefficient kyxs. The availability coefficient kyxs and the failure mode gzms are matched and correlated to form the safety index Aqzs.

[0088] The risk level determination module determines the corresponding risk level for each fault mode based on the degree and probability of the fault, thereby classifying and sorting the faults, and high-risk faults need to be dealt with first.

[0089] The improvement measure formulation module is used to formulate corresponding improvement measures for high-risk faults. These measures include technical optimization, backup mechanisms and fault recovery strategies, which aim to reduce the probability of faults or mitigate the impact of faults.

[0090] The testing and verification module is used to test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. By testing the system, the expected effects of the improvement measures are verified, thereby ensuring the stability and reliability of the system.

[0091] During system operation, the various functions of the intelligent traffic management system are analyzed. Faults and anomalies are identified and their causes determined through analysis of each module. Fault mode assessment is performed by collecting relevant data from the system's operation. The dataset is then analyzed to train a model using feature data. This model training yields three indices: risk index Fxzs, fault index Gzzs, and safety index Aqzs. Based on the impact and probability of each fault, a corresponding risk level is determined for each fault mode, allowing for fault classification and prioritization. High-risk faults require priority handling. For high-risk faults, corresponding improvement measures are developed to reduce the probability or mitigate the impact. The improved system is then tested and verified to ensure the effectiveness and reliability of these measures. Finally, system testing verifies the expected results of the improvements, ensuring the system's stability and reliability.

[0092] Example 2

[0093] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the functional analysis module includes a module decomposition unit, a functional description unit, and a module classification unit;

[0094] The module decomposition unit is used to decompose the intelligent traffic management system into various functional modules, dividing the complex intelligent traffic management system into relatively independent functional modules, including traffic monitoring, data analysis, signal control and navigation planning;

[0095] The functional description module is used to describe each functional module in detail, clarifying their role, function and task;

[0096] The module classification unit is used to classify and summarize functional modules based on their similarity and relevance, so as to better understand the structure and composition of the system.

[0097] The fault mode identification module includes a historical data analysis unit, a system testing unit, and a fault list generation unit.

[0098] The historical data analysis unit is used to analyze historical fault data, identify past fault modes and anomalies in the system, and identify common fault modes in the system through statistical analysis of historical fault data, so as to facilitate subsequent risk assessment and optimization measures.

[0099] The system testing unit is used to perform various tests on the system, simulate different operating scenarios, discover potential failure modes and abnormal behaviors, and verify the stability and performance of the system under different conditions through system testing and experiments, so as to identify possible failure situations.

[0100] The fault list generation unit is used to generate a fault list of the system based on historical data analysis and system test results. It integrates historical fault data and test results to form a fault list of the system, which can be used for subsequent risk assessment and improvement measures.

[0101] The information acquisition module includes a sensor data acquisition unit and an external data interface unit;

[0102] The sensor data acquisition unit is used to collect data from various sensors in the traffic management system. By connecting to various sensor devices, it can obtain the data required by the traffic management system in real time, including traffic flow, vehicle location and environmental information. It matches the weather type tqlx and the equipment impact result yxjg to obtain the weather factor tqyz.

[0103] Among them, the sequence information of the impact information of the weather on the equipment operating status is matched according to the weather type tqk, and the recorded sequence information is marked as the equipment impact result yxjg;

[0104] The external data interface unit is used to obtain data from external data sources, including weather data and traffic event data. By connecting to external data sources, the external data interface unit obtains real-time relevant data from external data interfaces to supplement the internal data of the system and help to more comprehensively analyze and evaluate traffic conditions.

[0105] Among them, the temperature factor Wdyz includes: real-time ambient temperature Hjwd and preset operating temperature Yswd; the weather factor Tqyz includes weather type tqlx and equipment impact result yxjg; the road factor dlyz includes road type dllx and surrounding building type jzlx; and the population factor rkyz includes the population density value around the road rkmd and the residential area size jzgm.

[0106] Example 3

[0107] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the fault impact assessment module includes a fault consequence analysis unit, a risk assessment unit, and an optimization scheme formulation unit;

[0108] The failure consequence analysis unit is used to build a data model based on the acquired dataset for analysis and prediction, in order to obtain the risk index Fxzs, failure index Gzzs, and safety index Aqzs.

[0109] The fault index Gzzs is obtained using the following formula:

[0110]

[0111]

[0112] Gzzs = Hjxs + Jtll + A;

[0113] In the formula, Hjxs represents the environmental coefficient, Jtll represents the traffic flow coefficient, Wdyz represents the temperature factor, Tqyz represents the weather factor, Hjwd represents the real-time environmental temperature, Yswd represents the preset operating temperature, tqlx represents the weather type, yxjg represents the equipment impact result, dlyz represents the road factor, dllx represents the road type, jzlx represents the surrounding building type, rkyz represents the population factor, rkmd represents the population density value around the road, jzgm represents the residential area size, Gzzs represents the failure index, Aqzs represents the safety index, Nx represents the service life of the equipment, α represents the temperature value, βa1, a2, a3 and a4 represent the weight values ​​of the environmental coefficient Hjys, the traffic flow coefficient Jtll and the service life of the equipment Nx respectively, and A represents the correction constant;

[0114] Wherein, 0.55≤Hjxs≤0.75, 0.55≤Jtll≤0.75, 0.35≤Nx≤0.55, 0.35≤Wdyz≤0.55, 0.35≤Tqyz≤0.55, 0.35≤Dlyz≤0.55, 0.35≤rkmd≤0.55, and a1+a2+a3+a4≥2, and A represents the correction constant;

[0115] The risk assessment unit is used to comprehensively assess various faults by combining the probability of fault occurrence and the consequences of faults, determine their risk level to the system, and formulate corresponding countermeasures.

[0116] The optimization scheme formulation unit is used to prioritize high-risk faults based on the risk assessment results, and formulate corresponding technical optimization measures, backup mechanism measures and fault recovery strategies to maximize the reliability and stability of the system.

[0117] The security index Aqzs is obtained using the following formula:

[0118]

[0119] In the formula, kyxs represents the availability coefficient, gzyz represents the failure factor, xfyz represents the repair factor, gzms represents the failure mode, w1 and w2 represent the weight values ​​of the availability coefficient kyxs and the failure mode gzms, respectively, and M represents the correction constant.

[0120] Wherein, 0.65≤Kyxs≤0.85, 0.45≤Gzms≤0.65, w1+w2≥1.0, and M represents the correction constant;

[0121] Among them, the available coefficients Kyxs include: fault factor gzyz and repair factor xfyz.

[0122] The risk level determination module includes a risk level assessment unit;

[0123] The risk level assessment unit compares the risk index Fxzs with a preset risk threshold to determine the risk level of the system.

[0124] By comparing the risk index Fxzs with the preset threshold standard, a graded strategy scheme is obtained:

[0125] With Fxzs≤19, we achieve optimization level one, which has extremely low risk and system failure rate. We can maintain the current state and only need to perform routine maintenance and monitoring on the system.

[0126] 20≤Fxzs≤39, achieving optimization level two, with low risk, requires preventative maintenance for potential low-risk faults, enhanced system monitoring and inspection, and timely handling of potential faults;

[0127] 40≤Fxzs≤59, achieving optimization level three, medium risk, strengthening fault monitoring and diagnosis, developing backup and emergency recovery plans, and optimizing and improving some functions of the system;

[0128] 60≤Fxzs≤79, achieving optimization level four, which is relatively high risk. Strengthen preventive measures for high-risk failures, optimize key modules and performance of the system, and strengthen backup and fault recovery strategies.

[0129] If 80≤Fxzs≤99, the system reaches optimization level 5, which is extremely high-risk. Emergency measures should be taken for high-risk faults, including immediate troubleshooting and handling, optimization of the overall system structure and architecture, and improvement of the system's fault tolerance and security.

[0130] Example 4

[0131] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: the improvement measure formulation module includes an optimization scheme generation unit and a scheme evaluation and implementation unit;

[0132] The optimization scheme generation unit is used to formulate targeted optimization schemes and improvement measures based on the risk level and the specific circumstances of the system.

[0133] The scheme evaluation and implementation unit is used to evaluate the formulated optimization scheme and implement it into the intelligent traffic management system.

[0134] The testing and verification module includes a functional testing unit and a performance verification unit;

[0135] The functional testing unit is used to perform functional tests on each functional module of the intelligent traffic management system to verify whether the system works normally according to the design requirements.

[0136] The performance verification unit verifies and evaluates the performance of the intelligent traffic management system to confirm whether the system's performance indicators meet the requirements.

[0137] Example 5

[0138] A reliability evaluation method and system for an intelligent traffic management system, please refer to... Figure 2 Specifically, it includes the following steps:

[0139] Step 1: Through detailed analysis of each functional module of the intelligent traffic management system, understand the overall structure and function of the system;

[0140] Step 2: By identifying the fault modes and abnormal situations that occur in the intelligent traffic management system, determine the causes and circumstances leading to the system failures;

[0141] Step 3: Collect relevant data during the operation of the intelligent traffic management system;

[0142] Step 4: By performing fault mode assessment on the information collection module and calculating and analyzing the dataset, model training is conducted using feature data to obtain the following: risk index Fxzs, fault index Gzzs, and safety index Aqzs.

[0143] Step 5: Determine the corresponding risk level for each failure mode based on the degree and probability of the failure, thereby classifying and ranking the failures;

[0144] Step Six: Develop corresponding improvement measures for high-risk faults to reduce the probability of faults or mitigate their impact;

[0145] Step 7: Test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. By testing the system, verify the expected effects of the improvement measures, thereby ensuring the stability and reliability of the system.

[0146] The method of this invention, through steps one through six, achieves a comprehensive understanding of the functions and composition of the intelligent traffic management system, clarifies the role and responsibility of each functional module, identifies system failure modes, and takes timely measures to improve system stability, ensuring that the intelligent traffic management system can effectively cope with potential failures and anomalies. It also ensures that this data is promptly transmitted to other functional modules for processing and analysis. Failure mode assessment is performed through information collection, and the dataset is calculated and analyzed. Model training is then conducted using feature data to obtain: a risk index Fxzs, a failure index Gzzs, and a safety index Aqzs. This comprehensively assesses the potential impact of system failures, determines the risk level of the failures, and formulates corresponding optimization schemes to ensure the stable operation of the intelligent traffic management system. By comparing the risk index with thresholds, corresponding optimization strategies can be formulated based on different risk levels. By comprehensively considering various system indicators and risk indices, the risk level of the system is quantitatively assessed and classified into different levels, leading to corresponding decisions and countermeasures. A comprehensive evaluation and implementation phase is then conducted.

[0147] Specific examples:

[0148] Suppose that in a certain intelligent traffic management system, the information collection module collects the following specific parameter values:

[0149] Temperature value Hjwd: 30℃, preset operating temperature Yswd: 25℃, weather type tqlx: sunny, equipment impact result yxjg: normal, road type dllx: urban main road, surrounding building type jzlx: commercial center, population density value rkmd: 500 people / km 2 Residential area size (jzgm): Large residential area; Failure rate (gzl): 0.02; Mean time between failures (MTBF) (wgzsc): 1000 hours; Failure rate (sxl): 0.005; Mean time to repair (xfsc): 4 hours; Failure mode (gzms): Hardware failure.

[0150] Substitute the above specific parameter values ​​into the formula to calculate the deformation coefficient. Example:

[0151] Calculate the temperature factor wdyz: =|30℃-25℃| / 25℃=0.2

[0152] Calculate the weather factor tqyz: tqyz = 0.5 (the weather factor corresponding to a sunny day; this example data is used here, and the actual value will be determined based on the specific weather type and the impact of the equipment).

[0153] Calculate the environmental factor hjxs: = 0.2 * 0.5 = 0.1

[0154] Calculate the road factor dlyz: dlyz = 0.8 (the road factor corresponding to urban arterial roads; this example uses sample data, but the actual factor is obtained by matching the specific road type and surrounding building types).

[0155] Calculate the population factor rkyz: rkyz = 0.6 (the population factor corresponding to a large residential area; this example data is used here, and the actual value is obtained by matching the specific population density value and the size of the residential area).

[0156] The traffic flow coefficient Jtll is calculated as 0.8 * 0.6 = 0.48.

[0157] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A reliability evaluation system for an intelligent traffic management system, characterized in that: It includes a functional analysis module, a fault mode identification module, an information acquisition module, a fault impact assessment module, a risk level determination module, an improvement measure formulation module, and a testing and verification module; The functional analysis module is used to perform detailed analysis of each functional module of the intelligent traffic management system to understand the overall structure and function of the system, including traffic monitoring, data analysis, signal control and navigation planning. The functional analysis module provides basic information for subsequent evaluation. The fault mode identification module is used to identify fault modes and abnormal situations that occur in the intelligent traffic management system. By analyzing each functional module, the causes and situations leading to system faults are determined. The information collection module is used to collect relevant data from the intelligent traffic management system during system operation, including fault information and performance data, and package them into a dataset to send to the fault impact assessment module. The fault impact assessment module evaluates the fault modes of the information collection module, calculates and analyzes the dataset, and then uses the feature data to train a model to obtain the following: risk index Fxzs, fault index Gzzs, and safety index Aqzs. In the formula, Fxzs represents the risk index, Gzzs represents the failure index, Aqzs represents the safety index, Nx represents the service life of the equipment, α represents the temperature value, and β represents the humidity value; The real-time ambient temperature Hjwd is compared with the preset operating temperature Yswd to obtain the temperature factor wdyz. The weather type tqlx and the equipment impact result yxjg are matched to obtain the weather factor tqyz. The temperature factor wdyz and the weather factor tqyz are correlated and fitted into the environmental coefficient hjxs. The road type dllx and the surrounding building type jzlx are matched to obtain the road factor dlyz. The population density value rkmd around the road and the residential area size jzgm are matched to obtain the population factor rkyz. The road factor dlyz and the population factor rkyz are correlated and fitted into the traffic flow coefficient Jtll. The failure rate gzl and the mean time between failures wgzsc are matched to obtain the failure factor gzyz. The failure rate sxl and the mean repair time xfsc are matched to obtain the repair factor xfyz. The failure factor gzyz and the repair factor xfyz are correlated to form the availability coefficient kyxs. The availability coefficient kyxs and the failure mode gzms are matched and correlated to form the safety index Aqzs. The risk level determination module determines the corresponding risk level for each fault mode based on the degree and probability of the fault, thereby classifying and sorting the faults, and high-risk faults need to be dealt with first. The improvement measure formulation module is used to formulate corresponding improvement measures for high-risk faults. These measures include technical optimization, backup mechanisms and fault recovery strategies, which aim to reduce the probability of faults or mitigate the impact of faults. The testing and verification module is used to test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. By testing the system, the expected effects of the improvement measures are verified, thereby ensuring the stability and reliability of the system.

2. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The functional analysis module includes a module decomposition unit, a functional description unit, and a module classification unit; The module decomposition unit is used to decompose the intelligent traffic management system into various functional modules, dividing the complex intelligent traffic management system into relatively independent functional modules, including traffic monitoring, data analysis, signal control and navigation planning; The functional description module is used to describe each functional module in detail, clarifying their role, function and task; The module classification unit is used to classify and summarize functional modules based on their similarity and relevance, so as to better understand the structure and composition of the system.

3. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The fault mode identification module includes a historical data analysis unit, a system testing unit, and a fault list generation unit. The historical data analysis unit is used to analyze historical fault data, identify past fault modes and anomalies in the system, and identify common fault modes in the system through statistical analysis of historical fault data, so as to facilitate subsequent risk assessment and optimization measures. The system testing unit is used to perform various tests on the system, simulate different operating scenarios, discover potential failure modes and abnormal behaviors, and verify the stability and performance of the system under different conditions through system testing and experiments, so as to identify possible failure situations. The fault list generation unit is used to generate a fault list of the system based on historical data analysis and system test results. It integrates historical fault data and test results to form a fault list of the system, which can be used for subsequent risk assessment and improvement measures.

4. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The information acquisition module includes a sensor data acquisition unit and an external data interface unit; The sensor data acquisition unit is used to collect data from various sensors in the traffic management system. By connecting to various sensor devices, it can obtain the data required by the traffic management system in real time, including traffic flow, vehicle location and environmental information. It matches the weather type tqlx and the equipment impact result yxjg to obtain the weather factor tqyz. Among them, the sequence information of the impact information of the weather on the equipment operating status is matched according to the weather type tqk, and the recorded sequence information is marked as the equipment impact result yxjg; The external data interface unit is used to obtain data from external data sources, including weather data and traffic event data. By connecting to external data sources, the external data interface unit obtains real-time relevant data from external data interfaces to supplement the internal data of the system and help to more comprehensively analyze and evaluate traffic conditions. Among them, the temperature factor Wdyz includes: real-time ambient temperature Hjwd and preset operating temperature Yswd; the weather factor Tqyz includes weather type tqlx and equipment impact result yxjg; the road factor dlyz includes road type dllx and surrounding building type jzlx; and the population factor rkyz includes the population density value around the road rkmd and the residential area size jzgm.

5. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The fault impact assessment module includes a fault consequence analysis unit, a risk assessment unit, and an optimization scheme formulation unit. The failure consequence analysis unit is used to build a data model based on the acquired dataset for analysis and prediction, in order to obtain the risk index Fxzs, failure index Gzzs, and safety index Aqzs. The fault index Gzzs is obtained using the following formula: Gzzs = Hjxs + Jtll + A; In the formula, Hjxs represents the environmental coefficient, Jtll represents the traffic flow coefficient, Wdyz represents the temperature factor, Tqyz represents the weather factor, Hjwd represents the real-time environmental temperature, Yswd represents the preset operating temperature, tqlx represents the weather type, yxjg represents the equipment impact result, dlyz represents the road factor, dllx represents the road type, jzlx represents the surrounding building type, rkyz represents the population factor, rkmd represents the population density value around the road, jzgm represents the residential area size, Gzzs represents the failure index, Aqzs represents the safety index, Nx represents the service life of the equipment, α represents the temperature value, a1, a2, a3 and a4 represent the weight values ​​of the temperature factor Wdyz, weather type tqlx, road factor dlyz and population factor rkyz respectively, and A represents the correction constant; Wherein, 0.55≤Hjxs≤0.75, 0.55≤Jtll≤0.75, 0.35≤Nx≤0.55, 0.35≤Wdyz≤0.55, 0.35≤Tqyz≤0.55, 0.35≤Dlyz≤0.55, 0.35≤rkmd≤0.55, and a1+a2+a3+a4≥2, and A represents the correction constant; The risk assessment unit is used to comprehensively assess various faults by combining the probability of fault occurrence and the consequences of faults, determine their risk level to the system, and formulate corresponding countermeasures. The optimization scheme formulation unit is used to prioritize high-risk faults based on the risk assessment results, and formulate corresponding technical optimization measures, backup mechanism measures and fault recovery strategies to maximize the reliability and stability of the system.

6. The reliability evaluation system for an intelligent traffic management system according to claim 5, characterized in that: The security index Aqzs is obtained using the following formula: In the formula, kyxs represents the availability coefficient, gzyz represents the failure factor, xfyz represents the repair factor, gzms represents the failure mode, w1 and w2 represent the weight values ​​of the availability coefficient kyxs and the failure mode gzms, respectively, and M represents the correction constant. Wherein, 0.65≤Kyxs≤0.85, 0.45≤Gzms≤0.65, w1+w2≥1.0, and M represents the correction constant; Among them, the available coefficients Kyxs include: fault factor gzyz and repair factor xfyz.

7. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The risk level determination module includes a risk level assessment unit; The risk level assessment unit compares the risk index Fxzs with a preset risk threshold to determine the risk level of the system. By comparing the risk index Fxzs with the preset threshold standard, a graded strategy scheme is obtained: If Fxzs≤19, optimization level 1 is obtained, the risk is extremely low, and the current state can be maintained; 20≤Fxzs≤39, achieving optimization level two, with low risk, and performing preventative maintenance for potential low-risk faults; 40≤Fxzs≤59, achieving optimization level three, medium risk, strengthening fault monitoring and diagnosis, and developing backup and emergency recovery plans; 60≤Fxzs≤79, achieving optimization level four, which carries a relatively high risk; therefore, it is necessary to strengthen preventive measures against high-risk faults. If 80≤Fxzs≤99, optimization level 5 is obtained, which is extremely risky, and emergency measures should be taken.

8. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The improvement measure formulation module includes an optimization scheme generation unit and a scheme evaluation and implementation unit; The optimization scheme generation unit is used to formulate targeted optimization schemes and improvement measures based on the risk level and the specific circumstances of the system. The scheme evaluation and implementation unit is used to evaluate the formulated optimization scheme and implement it into the intelligent traffic management system.

9. The reliability evaluation system for an intelligent traffic management system according to claim 1, characterized in that: The testing and verification module includes a functional testing unit and a performance verification unit; The functional testing unit is used to perform functional tests on each functional module of the intelligent traffic management system to verify whether the system works normally according to the design requirements. The performance verification unit verifies and evaluates the performance of the intelligent traffic management system to confirm whether the system's performance indicators meet the requirements.

10. The reliability evaluation method for an intelligent traffic management system according to claim 1, characterized in that: Includes the following steps: Step 1: Through detailed analysis of each functional module of the intelligent traffic management system, understand the overall structure and function of the system; Step 2: By identifying the fault modes and abnormal situations that occur in the intelligent traffic management system, determine the causes and circumstances leading to the system failures; Step 3: Collect relevant data during the operation of the intelligent traffic management system; Step 4: By performing fault mode assessment on the information collection module and calculating and analyzing the dataset, model training is conducted using feature data to obtain the following: risk index Fxzs, fault index Gzzs, and safety index Aqzs. Step 5: Determine the corresponding risk level for each failure mode based on the degree and probability of the failure, thereby classifying and ranking the failures; Step Six: Develop corresponding improvement measures for high-risk faults to reduce the probability of faults or mitigate their impact; Step 7: Test and verify the improved system to ensure the effectiveness and reliability of the improvement measures. By testing the system, verify the expected effects of the improvement measures, thereby ensuring the stability and reliability of the system.

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